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Preparation Process

To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.

Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.

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The Google Professional-Data-Engineer Exam is designed for professionals who have experience working with data processing systems in a business environment. Candidates should have a thorough understanding of data modeling, data warehousing, and data transformation. Additionally, they should have experience working with cloud-based data storage and processing systems such as Google Cloud Platform, AWS, or Microsoft Azure.

Google Certified Professional Data Engineer Exam Sample Questions (Q419-Q424):

NEW QUESTION # 419
You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

A. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
B. Store the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as
80% warm and 20% active.
C. Store and process the entire dataset in Cloud Bigtable.
D. Store and process the entire dataset in BigQuery.

Answer: B

NEW QUESTION # 420
Your neural network model is taking days to train. You want to increase the training speed. What can you do?

A. Subsample your training dataset.
B. Subsample your test dataset.
C. Increase the number of layers in your neural network.
D. Increase the number of input features to your model.

Answer: C

Explanation:
Explanation/Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-9f5d1c6f407d

NEW QUESTION # 421
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

A. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
B. Re-write the application to load accumulated data every 2 minutes.
C. Convert the streaming insert code to batch load for individual messages.
D. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Answer: D

Explanation:
Explanation
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue.
So We need to wait till it's written tio storage

NEW QUESTION # 422
Your company is in the process of migrating its on-premises data warehousing solutions to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply updates from multiple transactional database sources on a daily basis. With BigQuery, your company hopes to improve its handling of CDC so that changes to the source systems are available to query in BigQuery in near-real time using log- based CDC streams, while also optimizing for the performance of applying changes to the data warehouse.
Which two steps should they take to ensure that changes are available in the BigQuery reporting table with minimal latency while reducing compute overhead? (Choose two.)

A. Periodically use a DML MERGE to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
B. Insert each new CDC record and corresponding operation type in real time to the reporting table, and use a materialized view to expose only the newest version of each unique record.
C. Perform a DML INSERT, UPDATE, or DELETE to replicate each individual CDC record in real time directly on the reporting table.
D. Insert each new CDC record and corresponding operation type to a staging table in real time.
E. Periodically DELETE outdated records from the reporting table.

Answer: C,D

NEW QUESTION # 423
You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?

A. Enable the Object Versioning feature. Add a lifecycle rule.
B. Set a retention policy. Lock the retention policy.
C. Set a retention policy. Set the default storage class to Archive for long-term digital preservation.
D. Enable the Object Versioning feature. Create a copy in a bucket in a different region.

Answer: B

Explanation:
To ensure that important legal hold documents in a Cloud Storage bucket are not deleted or modified, the most effective method is to set and lock a retention policy. Here's why this is the best choice:
Retention Policy:
A retention policy defines a retention period during which objects in the bucket cannot be deleted or modified. This ensures data immutability.
Once a retention policy is set and locked, it cannot be removed or reduced, providing strong protection against accidental or malicious deletions.
Locking the Retention Policy:
Locking a retention policy ensures that the retention period cannot be changed. This action is permanent and guarantees that the specified retention period will be enforced.
Steps to Implement:
Set the Retention Policy:
Define a retention period for the bucket to ensure that all objects are protected for the required duration.
Lock the Retention Policy:
Lock the retention policy to prevent any modifications, ensuring the immutability of the documents.
Reference:
Cloud Storage Retention Policy Documentation
How to Set a Retention Policy

NEW QUESTION # 424
......

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